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Mastering Concurrency in Python

You're reading from Mastering Concurrency in Python Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781789343052

Length 446 pages

Edition 1st Edition

Languages

Python

Tools

Abstract

Concepts

Concurrency

Author (1):

Quan Nguyen

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Table of Contents (28) Chapters

Title Page

Copyright and Credits

Dedication

About Packt

Contributors

Preface

1. Advanced Introduction to Concurrent and Parallel Programming FREE CHAPTER

2. Amdahl's Law

3. Working with Threads in Python

4. Using the with Statement in Threads

5. Concurrent Web Requests

6. Working with Processes in Python

7. Reduction Operators in Processes

8. Concurrent Image Processing

9. Introduction to Asynchronous Programming

10. Implementing Asynchronous Programming in Python

11. Building Communication Channels with asyncio

12. Deadlocks

13. Starvation

14. Race Conditions

15. The Global Interpreter Lock

16. Designing Lock-Based and Mutex-Free Concurrent Data Structures

17. Memory Models and Operations on Atomic Types

18. Building a Server from Scratch

19. Testing, Debugging, and Scheduling Concurrent Applications

1. Assessments

2. Other Books You May Enjoy

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Index

Chapter 2. Amdahl's Law

Often used in discussions revolving around concurrent programs, Amdahl's Law explains the theoretical speedup of the execution of a program that be be expected when using concurrency. This chapter discusses the concept of Amdahl's Law, analyzes its formula that estimates the potential speedup, and replicates it in Python example code. The chapter also briefly covers the relationship between Amdahl's Law and the law of diminishing returns.

The following topics will be covered in this chapter:

Amdahl's Law
Formula and Interpretation
Relationship with the Law of Diminishing Returns
Simulation in Python

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Authors (1)

Quan Nguyen

Quan Nguyen

Quan Nguyen, the author of the first edition of this book, is a Python programmer with a strong passion for machine learning. He holds a dual degree in mathematics and computer science, with a minor in philosophy, earned from DePauw University. Quan is deeply involved in the Python community and has authored multiple Python books, contributing to the Python Software Foundation and regularly sharing insights on DataScience portal. He is currently pursuing a Ph.D. in computer science at Washington University in St. Louis.

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